An evaluation of models for predicting opponent positions in first-person shooter video games

A well-known Artificial Intelligence (AI) problem in video games is designing AI-controlled humanoid characters. It is desirable for these characters to appear both skillful and believably human-like. Many games address the former objective by providing their agents with unfair advantages. Although challenging, these agents are frustrating to humans who perceive the AI to be cheating. In this paper we evaluate hidden semi-Markov models and particle filters as a means for predicting opponent positions. Our results show that these models can perform with similar or better accuracy than the average human expert in the game Counter-Strike: Source. Furthermore, the mistakes these models make are more human-like than perfect predictions.